Rural chronic disease research patterns in the United Kingdom, United States, Canada, Australia and New Zealand: a systematic integrative review
Bibliographic record
Abstract
BACKGROUND: People living in rural and remote communities commonly experience significant health disadvantages. Geographical barriers and reduced specialist and generalist services impact access to care when compared with metropolitan context. Innovative models of care have been developed for people living with chronic diseases in rural areas with the goal of overcoming these inequities. The aim of this paper was to describe the characteristics and outcomes of studies investigating innovative models of care for people living with chronic disease in rural areas of developed countries where a metropolitan comparator was included. METHODS: An integrative systematic review was undertaken. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was used to understand the empirical and theoretical data on clinical outcomes for people living with chronic disease in rural compared with metropolitan contexts and their models of care in Australia, New Zealand, United States, Canada and the United Kingdom. RESULTS: Literature searching revealed 620 articles published in English between 1st January 2000 and 31st March 2019. One hundred sixty were included in the review including 68 from the United States, 59 from Australia and New Zealand (5), 21 from Canada and 11 from the United Kingdom and Ireland. 53% (84) focused on cardiovascular disease; 27% (43) diabetes mellitus; 8% (12) chronic obstructive pulmonary disease; and 13% (27) chronic kidney disease. Mortality was only reported in 10% (16) of studies and only 18% (29) reported data on Indigenous populations. CONCLUSIONS: This integrated review reveals that the published literature on common chronic health issues pertaining to rural and remote populations is largely descriptive. Only a small number of publications focus on mortality and comparative health outcomes from health care models in both urban and non-urban populations. Innovative service models and telehealth are together well represented in the published literature but data on health outcomes is relatively sparse. There is significant scope for further directly comparative studies detailing the effect of service delivery models on the health outcomes of urban and rural populations. We believe that such data would further knowledge in this field and help to break the deadly synergy between increased rurality and poorer outcomes for people with chronic disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".